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Efficient Top-k Search Across Heterogeneous XML Data Sources

机译:跨异构XML数据源的高效Top-k搜索

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An important issue arising from XML query relaxation is how to efficiently search the top-k best answers from a large number of XML data sources, while minimizing the searching cost, i.e., finding the k matches with the highest computed scores by only traversing part of the documents. This paper resolves this issue by proposing a bound-threshold based scheduling strategy. It can answer a top-k XML query as early as possible by dynamically scheduling the query over XML documents. In this work, the total amount of documents that need to be visited can be greatly reduced by skipping those documents that will not produce the desired results with the bound-threshold strategy. Furthermore, most of the candidates in each visited document can also be pruned based on the intermediate results. Most importantly, the partial results can be output immediately during the query execution, rather than waiting for the end of all results to be determined. Our experimental results show that our query scheduling and processing strategies are, both practical and efficient.
机译:XML查询放宽引起的一个重要问题是如何从大量XML数据源中高效搜索前k个最佳答案,同时又将搜索成本降至最低,即仅遍历部分XML即可找到计算得分最高的k个匹配项。文件。本文通过提出基于边界阈值的调度策略来解决此问题。通过在XML文档上动态调度查询,它可以尽早回答top-k XML查询。在这项工作中,通过使用阈值限制策略跳过那些不会产生期望结果的文档,可以大大减少需要访问的文档总数。此外,每个访问文档中的大多数候选项也可以根据中间结果进行修剪。最重要的是,可以在查询执行期间立即输出部分结果,而不必等待所有结果的确定。我们的实验结果表明,我们的查询调度和处理策略既实用又有效。

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